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                <h2 id="一、思路"><a href="#一、思路" class="headerlink" title="一、思路"></a>一、思路</h2><h2 id="二、进程"><a href="#二、进程" class="headerlink" title="二、进程"></a>二、进程</h2><p>更换了数据集，数据集下载的地址，<a target="_blank" rel="noopener" href="https://download.csdn.net/download/qq_37972530/10580382?depth_1-utm_source=distribute.pc_relevant.none-task&amp;utm_source=distribute.pc_relevant.none-task">https://download.csdn.net/download/qq_37972530/10580382?depth_1-utm_source=distribute.pc_relevant.none-task&amp;utm_source=distribute.pc_relevant.none-task</a></p>
<p>选择玫瑰、梅花、牵牛花、桃花作为数据集</p>
<p>吴恩达老师的视频中，如果当数据量不是很大的时候（万级别以下）的时候将训练集、验证集以及测试集划分为6：2：2；若是数据很大，可以将训练集、验证集、测试集比例调整为98：1：1；但是当可用的数据很少的情况下也可以使用一些高级的方法，比如留出方，K折交叉验证等。<a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/48976706">from</a></p>
<p>根据上文划分文件夹</p>
<h2 id="三、参考"><a href="#三、参考" class="headerlink" title="三、参考"></a>三、参考</h2><h3 id="1-denny的学习专栏"><a href="#1-denny的学习专栏" class="headerlink" title="1.denny的学习专栏"></a>1.denny的学习专栏</h3><p>这位大佬的<a target="_blank" rel="noopener" href="https://www.cnblogs.com/denny402/">博客</a>里有关于tensorflow的很多内容，并且有<a target="_blank" rel="noopener" href="https://www.cnblogs.com/denny402/p/6931338.html">花卉识别项目</a>的源代码和介绍，很有参考价值。为了内容丢失，已放在到博客里。</p>
<span id="more"></span>

<h3 id="2-Plain-and-Simple-Estimators"><a href="#2-Plain-and-Simple-Estimators" class="headerlink" title="2.Plain and Simple Estimators"></a>2.Plain and Simple Estimators</h3><p>这个小视频<a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/30722498%E7%AE%80%E5%8D%95%E4%BB%8B%E7%BB%8D%E4%BA%86%E8%AF%A5%E9%A1%B9%E7%9B%AE%EF%BC%8C%E5%B9%B6%E7%AE%80%E5%8D%95%E8%AE%B2%E8%A7%A3%E4%BA%86%E4%BB%A3%E7%A0%81%EF%BC%8Cgithub%E5%B7%B2follow">https://zhuanlan.zhihu.com/p/30722498简单介绍了该项目，并简单讲解了代码，github已follow</a>.</p>
<h2 id="四、成功案列"><a href="#四、成功案列" class="headerlink" title="四、成功案列"></a>四、成功案列</h2><p>（1）</p>
<h3 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h3><p>本文为一个利用卷积神经网络实现花卉分类的项目，因此不会过多介绍卷积神经网络的基本知识。此项目建立在了解卷积神经网络进行图像分类的原理上进行的。</p>
<h3 id="项目简介"><a href="#项目简介" class="headerlink" title="项目简介"></a>项目简介</h3><p>本项目为一个图像识别项目，基于tensorflow，利用CNN网络实现识别四种花的种类。<br>使用tensorflow进行一个完整的图像识别。项目包括对数据集的处理，从硬盘读取数据，CNN网络的定义，训练过程以及利用实际测试数据对训练好的模型结果进行测试功能。</p>
<h3 id="准备训练数据。"><a href="#准备训练数据。" class="headerlink" title="准备训练数据。"></a>准备训练数据。</h3><p>训练数据存放路径为: ‘D:/ML/flower/input_data’<br>训练模型存储路径为:’D:/ML/flower/save/‘<br>测试样本路径及文件名为:’D:/ML/flower/flower_photos/roses/**.jpg‘<br>测试用图片文件从训练数据中任意拷贝一张即可。</p>
<p>训练数据如图<br><img src="https://img-blog.csdnimg.cn/20190904020743785.png" alt="在这里插入图片描述"><br>以roses种类的训练数据为例，文件夹内部均为该种类花的图像文件</p>
<p><img src="https://img-blog.csdnimg.cn/20190904020812573.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NyaW1zb25L,size_16,color_FFFFFF,t_70" alt="在这里插入图片描述"></p>
<h3 id="模块组成"><a href="#模块组成" class="headerlink" title="模块组成"></a>模块组成</h3><p>示例代码主要由四个模块组成：<br>input_data.py——图像特征提取模块，模块生成四种花的品类图片路径及对应标签的List<br>model.py——模型模块，构建完整的CNN模型<br>train.py——训练模块，训练模型，并保存训练模型结果<br>test.py——测试模块，测试模型对图片识别的准确度</p>
<p>项目模块执行顺序</p>
<ul>
<li>运行train.py开始训练。</li>
<li>训练完成后- 运行test.py，查看实际测试结果</li>
</ul>
<p>input_data.py——图像特征提取模块，模块生成四种花的品类图片路径及对应标签的List</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token keyword">import</span> os
<span class="token keyword">import</span> math
<span class="token keyword">import</span> numpy <span class="token keyword">as</span> np
<span class="token keyword">import</span> tensorflow <span class="token keyword">as</span> tf
<span class="token keyword">import</span> matplotlib<span class="token punctuation">.</span>pyplot <span class="token keyword">as</span> plt

<span class="token comment"># -----------------生成图片路径和标签的List------------------------------------</span>
train_dir <span class="token operator">=</span> <span class="token string">'D:/ML/flower/input_data'</span>

roses <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
label_roses <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
tulips <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
label_tulips <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
dandelion <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
label_dandelion <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
sunflowers <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
label_sunflowers <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>

<p>定义函数get_files,获取图片列表及标签列表</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token comment"># step1：获取所有的图片路径名，存放到</span>
<span class="token comment"># 对应的列表中，同时贴上标签，存放到label列表中。</span>
<span class="token keyword">def</span> <span class="token function">get_files</span><span class="token punctuation">(</span>file_dir<span class="token punctuation">,</span> ratio<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">for</span> <span class="token builtin">file</span> <span class="token keyword">in</span> os<span class="token punctuation">.</span>listdir<span class="token punctuation">(</span>file_dir <span class="token operator">+</span> <span class="token string">'/roses'</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
        roses<span class="token punctuation">.</span>append<span class="token punctuation">(</span>file_dir <span class="token operator">+</span> <span class="token string">'/roses'</span> <span class="token operator">+</span> <span class="token string">'/'</span> <span class="token operator">+</span> <span class="token builtin">file</span><span class="token punctuation">)</span>
        label_roses<span class="token punctuation">.</span>append<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span>
    <span class="token keyword">for</span> <span class="token builtin">file</span> <span class="token keyword">in</span> os<span class="token punctuation">.</span>listdir<span class="token punctuation">(</span>file_dir <span class="token operator">+</span> <span class="token string">'/tulips'</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
        tulips<span class="token punctuation">.</span>append<span class="token punctuation">(</span>file_dir <span class="token operator">+</span> <span class="token string">'/tulips'</span> <span class="token operator">+</span> <span class="token string">'/'</span> <span class="token operator">+</span> <span class="token builtin">file</span><span class="token punctuation">)</span>
        label_tulips<span class="token punctuation">.</span>append<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">)</span>
    <span class="token keyword">for</span> <span class="token builtin">file</span> <span class="token keyword">in</span> os<span class="token punctuation">.</span>listdir<span class="token punctuation">(</span>file_dir <span class="token operator">+</span> <span class="token string">'/dandelion'</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
        dandelion<span class="token punctuation">.</span>append<span class="token punctuation">(</span>file_dir <span class="token operator">+</span> <span class="token string">'/dandelion'</span> <span class="token operator">+</span> <span class="token string">'/'</span> <span class="token operator">+</span> <span class="token builtin">file</span><span class="token punctuation">)</span>
        label_dandelion<span class="token punctuation">.</span>append<span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">)</span>
    <span class="token keyword">for</span> <span class="token builtin">file</span> <span class="token keyword">in</span> os<span class="token punctuation">.</span>listdir<span class="token punctuation">(</span>file_dir <span class="token operator">+</span> <span class="token string">'/sunflowers'</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
        sunflowers<span class="token punctuation">.</span>append<span class="token punctuation">(</span>file_dir <span class="token operator">+</span> <span class="token string">'/sunflowers'</span> <span class="token operator">+</span> <span class="token string">'/'</span> <span class="token operator">+</span> <span class="token builtin">file</span><span class="token punctuation">)</span>
        label_sunflowers<span class="token punctuation">.</span>append<span class="token punctuation">(</span><span class="token number">3</span><span class="token punctuation">)</span>
        <span class="token comment"># step2：对生成的图片路径和标签List做打乱处理</span>
    image_list <span class="token operator">=</span> np<span class="token punctuation">.</span>hstack<span class="token punctuation">(</span><span class="token punctuation">(</span>roses<span class="token punctuation">,</span> tulips<span class="token punctuation">,</span> dandelion<span class="token punctuation">,</span> sunflowers<span class="token punctuation">)</span><span class="token punctuation">)</span>
    label_list <span class="token operator">=</span> np<span class="token punctuation">.</span>hstack<span class="token punctuation">(</span><span class="token punctuation">(</span>label_roses<span class="token punctuation">,</span> label_tulips<span class="token punctuation">,</span> label_dandelion<span class="token punctuation">,</span> label_sunflowers<span class="token punctuation">)</span><span class="token punctuation">)</span>

    <span class="token comment"># 利用shuffle打乱顺序</span>
    temp <span class="token operator">=</span> np<span class="token punctuation">.</span>array<span class="token punctuation">(</span><span class="token punctuation">[</span>image_list<span class="token punctuation">,</span> label_list<span class="token punctuation">]</span><span class="token punctuation">)</span>
    temp <span class="token operator">=</span> temp<span class="token punctuation">.</span>transpose<span class="token punctuation">(</span><span class="token punctuation">)</span>
    np<span class="token punctuation">.</span>random<span class="token punctuation">.</span>shuffle<span class="token punctuation">(</span>temp<span class="token punctuation">)</span>


    <span class="token comment"># 将所有的img和lab转换成list</span>
    all_image_list <span class="token operator">=</span> <span class="token builtin">list</span><span class="token punctuation">(</span>temp<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
    all_label_list <span class="token operator">=</span> <span class="token builtin">list</span><span class="token punctuation">(</span>temp<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
        <span class="token comment"># 将所得List分为两部分，一部分用来训练tra，一部分用来测试val</span>
    <span class="token comment"># ratio是测试集的比例</span>
    n_sample <span class="token operator">=</span> <span class="token builtin">len</span><span class="token punctuation">(</span>all_label_list<span class="token punctuation">)</span>
    n_val <span class="token operator">=</span> <span class="token builtin">int</span><span class="token punctuation">(</span>math<span class="token punctuation">.</span>ceil<span class="token punctuation">(</span>n_sample <span class="token operator">*</span> ratio<span class="token punctuation">)</span><span class="token punctuation">)</span>  <span class="token comment"># 测试样本数</span>
    n_train <span class="token operator">=</span> n_sample <span class="token operator">-</span> n_val  <span class="token comment"># 训练样本数</span>

    tra_images <span class="token operator">=</span> all_image_list<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">:</span>n_train<span class="token punctuation">]</span>
    tra_labels <span class="token operator">=</span> all_label_list<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">:</span>n_train<span class="token punctuation">]</span>
    tra_labels <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token builtin">int</span><span class="token punctuation">(</span><span class="token builtin">float</span><span class="token punctuation">(</span>i<span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token keyword">for</span> i <span class="token keyword">in</span> tra_labels<span class="token punctuation">]</span>
    val_images <span class="token operator">=</span> all_image_list<span class="token punctuation">[</span>n_train<span class="token punctuation">:</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">]</span>
    val_labels <span class="token operator">=</span> all_label_list<span class="token punctuation">[</span>n_train<span class="token punctuation">:</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">]</span>
    val_labels <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token builtin">int</span><span class="token punctuation">(</span><span class="token builtin">float</span><span class="token punctuation">(</span>i<span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token keyword">for</span> i <span class="token keyword">in</span> val_labels<span class="token punctuation">]</span>

    <span class="token keyword">return</span> tra_images<span class="token punctuation">,</span> tra_labels<span class="token punctuation">,</span> val_images<span class="token punctuation">,</span> val_labels
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>

<p>定义函数get_batch,生成训练批次数据</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token comment"># --------------------生成Batch----------------------------------------------</span>

<span class="token comment"># step1：将上面生成的List传入get_batch() ，转换类型，产生一个输入队列queue，因为img和lab</span>
<span class="token comment"># 是分开的，所以使用tf.train.slice_input_producer()，然后用tf.read_file()从队列中读取图像</span>
<span class="token comment">#   image_W, image_H, ：设置好固定的图像高度和宽度</span>
<span class="token comment">#   设置batch_size：每个batch要放多少张图片</span>
<span class="token comment">#   capacity：一个队列最大多少</span>
定义函数get_batch<span class="token punctuation">,</span>生成训练批次数据
<span class="token keyword">def</span> <span class="token function">get_batch</span><span class="token punctuation">(</span>image<span class="token punctuation">,</span> label<span class="token punctuation">,</span> image_W<span class="token punctuation">,</span> image_H<span class="token punctuation">,</span> batch_size<span class="token punctuation">,</span> capacity<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token comment"># 转换类型</span>
    image <span class="token operator">=</span> tf<span class="token punctuation">.</span>cast<span class="token punctuation">(</span>image<span class="token punctuation">,</span> tf<span class="token punctuation">.</span>string<span class="token punctuation">)</span>
    label <span class="token operator">=</span> tf<span class="token punctuation">.</span>cast<span class="token punctuation">(</span>label<span class="token punctuation">,</span> tf<span class="token punctuation">.</span>int32<span class="token punctuation">)</span>

    <span class="token comment"># make an input queue</span>
    input_queue <span class="token operator">=</span> tf<span class="token punctuation">.</span>train<span class="token punctuation">.</span>slice_input_producer<span class="token punctuation">(</span><span class="token punctuation">[</span>image<span class="token punctuation">,</span> label<span class="token punctuation">]</span><span class="token punctuation">)</span>

    label <span class="token operator">=</span> input_queue<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span>
    image_contents <span class="token operator">=</span> tf<span class="token punctuation">.</span>read_file<span class="token punctuation">(</span>input_queue<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">)</span>  <span class="token comment"># read img from a queue</span>

    <span class="token comment"># step2：将图像解码，不同类型的图像不能混在一起，要么只用jpeg，要么只用png等。</span>
    image <span class="token operator">=</span> tf<span class="token punctuation">.</span>image<span class="token punctuation">.</span>decode_jpeg<span class="token punctuation">(</span>image_contents<span class="token punctuation">,</span> channels<span class="token operator">=</span><span class="token number">3</span><span class="token punctuation">)</span>
        <span class="token comment"># step3：数据预处理，对图像进行旋转、缩放、裁剪、归一化等操作，让计算出的模型更健壮。</span>
    image <span class="token operator">=</span> tf<span class="token punctuation">.</span>image<span class="token punctuation">.</span>resize_image_with_crop_or_pad<span class="token punctuation">(</span>image<span class="token punctuation">,</span> image_W<span class="token punctuation">,</span> image_H<span class="token punctuation">)</span>
    image <span class="token operator">=</span> tf<span class="token punctuation">.</span>image<span class="token punctuation">.</span>per_image_standardization<span class="token punctuation">(</span>image<span class="token punctuation">)</span>

    <span class="token comment"># step4：生成batch</span>
    <span class="token comment"># image_batch: 4D tensor [batch_size, width, height, 3],dtype=tf.float32</span>
    <span class="token comment"># label_batch: 1D tensor [batch_size], dtype=tf.int32</span>
    image_batch<span class="token punctuation">,</span> label_batch <span class="token operator">=</span> tf<span class="token punctuation">.</span>train<span class="token punctuation">.</span>batch<span class="token punctuation">(</span><span class="token punctuation">[</span>image<span class="token punctuation">,</span> label<span class="token punctuation">]</span><span class="token punctuation">,</span>
                                              batch_size<span class="token operator">=</span>batch_size<span class="token punctuation">,</span>
                                              num_threads<span class="token operator">=</span><span class="token number">32</span><span class="token punctuation">,</span>
                                              capacity<span class="token operator">=</span>capacity<span class="token punctuation">)</span>
    <span class="token comment"># 重新排列label，行数为[batch_size]</span>
    label_batch <span class="token operator">=</span> tf<span class="token punctuation">.</span>reshape<span class="token punctuation">(</span>label_batch<span class="token punctuation">,</span> <span class="token punctuation">[</span>batch_size<span class="token punctuation">]</span><span class="token punctuation">)</span>
    image_batch <span class="token operator">=</span> tf<span class="token punctuation">.</span>cast<span class="token punctuation">(</span>image_batch<span class="token punctuation">,</span> tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>
    <span class="token keyword">return</span> image_batch<span class="token punctuation">,</span> label_batch
    
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>

<p>model.py——CN模型构建</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token keyword">import</span> tensorflow <span class="token keyword">as</span> tf

<span class="token comment">#定义函数infence，定义CNN网络结构</span>
<span class="token comment">#卷积神经网络，卷积加池化*2，全连接*2，softmax分类</span>
<span class="token comment">#卷积层1</span>
<span class="token keyword">def</span> <span class="token function">inference</span><span class="token punctuation">(</span>images<span class="token punctuation">,</span> batch_size<span class="token punctuation">,</span> n_classes<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>variable_scope<span class="token punctuation">(</span><span class="token string">'conv1'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> scope<span class="token punctuation">:</span>
        weights <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>truncated_normal<span class="token punctuation">(</span>shape<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">3</span><span class="token punctuation">,</span><span class="token number">3</span><span class="token punctuation">,</span><span class="token number">3</span><span class="token punctuation">,</span><span class="token number">64</span><span class="token punctuation">]</span><span class="token punctuation">,</span>stddev<span class="token operator">=</span><span class="token number">1.0</span><span class="token punctuation">,</span>dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span><span class="token punctuation">,</span>
                             name <span class="token operator">=</span> <span class="token string">'weights'</span><span class="token punctuation">,</span>dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>
        biases <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>constant<span class="token punctuation">(</span>value<span class="token operator">=</span><span class="token number">0.1</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">,</span> shape<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">64</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
                             name<span class="token operator">=</span><span class="token string">'biases'</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>
        conv <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>conv2d<span class="token punctuation">(</span>images<span class="token punctuation">,</span> weights<span class="token punctuation">,</span> strides<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> padding<span class="token operator">=</span><span class="token string">'SAME'</span><span class="token punctuation">)</span>
        pre_activation <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>bias_add<span class="token punctuation">(</span>conv<span class="token punctuation">,</span> biases<span class="token punctuation">)</span>
        conv1 <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>relu<span class="token punctuation">(</span>pre_activation<span class="token punctuation">,</span> name<span class="token operator">=</span>scope<span class="token punctuation">.</span>name<span class="token punctuation">)</span>

    <span class="token comment"># 池化层1</span>
    <span class="token comment"># 3x3最大池化，步长strides为2，池化后执行lrn()操作，局部响应归一化，对训练有利。</span>


    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>variable_scope<span class="token punctuation">(</span><span class="token string">'pooling1_lrn'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> scope<span class="token punctuation">:</span>
        pool1 <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>max_pool<span class="token punctuation">(</span>conv1<span class="token punctuation">,</span> ksize<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> strides<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> padding<span class="token operator">=</span><span class="token string">'SAME'</span><span class="token punctuation">,</span> name<span class="token operator">=</span><span class="token string">'pooling1'</span><span class="token punctuation">)</span>
        norm1 <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>lrn<span class="token punctuation">(</span>pool1<span class="token punctuation">,</span> depth_radius<span class="token operator">=</span><span class="token number">4</span><span class="token punctuation">,</span> bias<span class="token operator">=</span><span class="token number">1.0</span><span class="token punctuation">,</span> alpha<span class="token operator">=</span><span class="token number">0.001</span> <span class="token operator">/</span> <span class="token number">9.0</span><span class="token punctuation">,</span> beta<span class="token operator">=</span><span class="token number">0.75</span><span class="token punctuation">,</span> name<span class="token operator">=</span><span class="token string">'norm1'</span><span class="token punctuation">)</span>

    <span class="token comment"># 卷积层2</span>
    <span class="token comment"># 16个3x3的卷积核（16通道），padding=’SAME’，表示padding后卷积的图与原图尺寸一致，激活函数relu()</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>variable_scope<span class="token punctuation">(</span><span class="token string">'conv2'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> scope<span class="token punctuation">:</span>
        weights <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>truncated_normal<span class="token punctuation">(</span>shape<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">64</span><span class="token punctuation">,</span> <span class="token number">16</span><span class="token punctuation">]</span><span class="token punctuation">,</span> stddev<span class="token operator">=</span><span class="token number">0.1</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span><span class="token punctuation">,</span>
                              name<span class="token operator">=</span><span class="token string">'weights'</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>

        biases <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>constant<span class="token punctuation">(</span>value<span class="token operator">=</span><span class="token number">0.1</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">,</span> shape<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">16</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
                             name<span class="token operator">=</span><span class="token string">'biases'</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>

        conv <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>conv2d<span class="token punctuation">(</span>norm1<span class="token punctuation">,</span> weights<span class="token punctuation">,</span> strides<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> padding<span class="token operator">=</span><span class="token string">'SAME'</span><span class="token punctuation">)</span>
        pre_activation <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>bias_add<span class="token punctuation">(</span>conv<span class="token punctuation">,</span> biases<span class="token punctuation">)</span>
        conv2 <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>relu<span class="token punctuation">(</span>pre_activation<span class="token punctuation">,</span> name<span class="token operator">=</span><span class="token string">'conv2'</span><span class="token punctuation">)</span>

    <span class="token comment"># 池化层2</span>
    <span class="token comment"># 3x3最大池化，步长strides为2，池化后执行lrn()操作，</span>
    <span class="token comment"># pool2 and norm2</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>variable_scope<span class="token punctuation">(</span><span class="token string">'pooling2_lrn'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> scope<span class="token punctuation">:</span>
        norm2 <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>lrn<span class="token punctuation">(</span>conv2<span class="token punctuation">,</span> depth_radius<span class="token operator">=</span><span class="token number">4</span><span class="token punctuation">,</span> bias<span class="token operator">=</span><span class="token number">1.0</span><span class="token punctuation">,</span> alpha<span class="token operator">=</span><span class="token number">0.001</span> <span class="token operator">/</span> <span class="token number">9.0</span><span class="token punctuation">,</span> beta<span class="token operator">=</span><span class="token number">0.75</span><span class="token punctuation">,</span> name<span class="token operator">=</span><span class="token string">'norm2'</span><span class="token punctuation">)</span>
        pool2 <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>max_pool<span class="token punctuation">(</span>norm2<span class="token punctuation">,</span> ksize<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> strides<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> padding<span class="token operator">=</span><span class="token string">'SAME'</span><span class="token punctuation">,</span> name<span class="token operator">=</span><span class="token string">'pooling2'</span><span class="token punctuation">)</span>

    <span class="token comment"># 全连接层3</span>
    <span class="token comment"># 128个神经元，将之前pool层的输出reshape成一行，激活函数relu()</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>variable_scope<span class="token punctuation">(</span><span class="token string">'local3'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> scope<span class="token punctuation">:</span>
        reshape <span class="token operator">=</span> tf<span class="token punctuation">.</span>reshape<span class="token punctuation">(</span>pool2<span class="token punctuation">,</span> shape<span class="token operator">=</span><span class="token punctuation">[</span>batch_size<span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
        dim <span class="token operator">=</span> reshape<span class="token punctuation">.</span>get_shape<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">.</span>value
        weights <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>truncated_normal<span class="token punctuation">(</span>shape<span class="token operator">=</span><span class="token punctuation">[</span>dim<span class="token punctuation">,</span> <span class="token number">128</span><span class="token punctuation">]</span><span class="token punctuation">,</span> stddev<span class="token operator">=</span><span class="token number">0.005</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span><span class="token punctuation">,</span>
                              name<span class="token operator">=</span><span class="token string">'weights'</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>

        biases <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>constant<span class="token punctuation">(</span>value<span class="token operator">=</span><span class="token number">0.1</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">,</span> shape<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">128</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
                             name<span class="token operator">=</span><span class="token string">'biases'</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>

        local3 <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>relu<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>matmul<span class="token punctuation">(</span>reshape<span class="token punctuation">,</span> weights<span class="token punctuation">)</span> <span class="token operator">+</span> biases<span class="token punctuation">,</span> name<span class="token operator">=</span>scope<span class="token punctuation">.</span>name<span class="token punctuation">)</span>

    <span class="token comment"># 全连接层4</span>
    <span class="token comment"># 128个神经元，激活函数relu()</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>variable_scope<span class="token punctuation">(</span><span class="token string">'local4'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> scope<span class="token punctuation">:</span>
        weights <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>truncated_normal<span class="token punctuation">(</span>shape<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">128</span><span class="token punctuation">,</span> <span class="token number">128</span><span class="token punctuation">]</span><span class="token punctuation">,</span> stddev<span class="token operator">=</span><span class="token number">0.005</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span><span class="token punctuation">,</span>
                              name<span class="token operator">=</span><span class="token string">'weights'</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>

        biases <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>constant<span class="token punctuation">(</span>value<span class="token operator">=</span><span class="token number">0.1</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">,</span> shape<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">128</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
                             name<span class="token operator">=</span><span class="token string">'biases'</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>

        local4 <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>relu<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>matmul<span class="token punctuation">(</span>local3<span class="token punctuation">,</span> weights<span class="token punctuation">)</span> <span class="token operator">+</span> biases<span class="token punctuation">,</span> name<span class="token operator">=</span><span class="token string">'local4'</span><span class="token punctuation">)</span>

    <span class="token comment"># dropout层</span>
    <span class="token comment">#    with tf.variable_scope('dropout') as scope:</span>
    <span class="token comment">#        drop_out = tf.nn.dropout(local4, 0.8)</span>

    <span class="token comment"># Softmax回归层</span>
    <span class="token comment"># 将前面的FC层输出，做一个线性回归，计算出每一类的得分</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>variable_scope<span class="token punctuation">(</span><span class="token string">'softmax_linear'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> scope<span class="token punctuation">:</span>
        weights <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>truncated_normal<span class="token punctuation">(</span>shape<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">128</span><span class="token punctuation">,</span> n_classes<span class="token punctuation">]</span><span class="token punctuation">,</span> stddev<span class="token operator">=</span><span class="token number">0.005</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span><span class="token punctuation">,</span>
                              name<span class="token operator">=</span><span class="token string">'softmax_linear'</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>

        biases <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>constant<span class="token punctuation">(</span>value<span class="token operator">=</span><span class="token number">0.1</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">,</span> shape<span class="token operator">=</span><span class="token punctuation">[</span>n_classes<span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
                             name<span class="token operator">=</span><span class="token string">'biases'</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>

        softmax_linear <span class="token operator">=</span> tf<span class="token punctuation">.</span>add<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>matmul<span class="token punctuation">(</span>local4<span class="token punctuation">,</span> weights<span class="token punctuation">)</span><span class="token punctuation">,</span> biases<span class="token punctuation">,</span> name<span class="token operator">=</span><span class="token string">'softmax_linear'</span><span class="token punctuation">)</span>

    <span class="token keyword">return</span> softmax_linear


<span class="token comment"># -----------------------------------------------------------------------------</span>
<span class="token comment"># loss计算</span>
<span class="token comment"># 传入参数：logits，网络计算输出值。labels，真实值，在这里是0或者1</span>
<span class="token comment"># 返回参数：loss，损失值</span>
<span class="token keyword">def</span> <span class="token function">losses</span><span class="token punctuation">(</span>logits<span class="token punctuation">,</span> labels<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>variable_scope<span class="token punctuation">(</span><span class="token string">'loss'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> scope<span class="token punctuation">:</span>
        cross_entropy <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>sparse_softmax_cross_entropy_with_logits<span class="token punctuation">(</span>logits<span class="token operator">=</span>logits<span class="token punctuation">,</span> labels<span class="token operator">=</span>labels<span class="token punctuation">,</span>
                                                                       name<span class="token operator">=</span><span class="token string">'xentropy_per_example'</span><span class="token punctuation">)</span>
        loss <span class="token operator">=</span> tf<span class="token punctuation">.</span>reduce_mean<span class="token punctuation">(</span>cross_entropy<span class="token punctuation">,</span> name<span class="token operator">=</span><span class="token string">'loss'</span><span class="token punctuation">)</span>
        tf<span class="token punctuation">.</span>summary<span class="token punctuation">.</span>scalar<span class="token punctuation">(</span>scope<span class="token punctuation">.</span>name <span class="token operator">+</span> <span class="token string">'/loss'</span><span class="token punctuation">,</span> loss<span class="token punctuation">)</span>
    <span class="token keyword">return</span> loss


<span class="token comment"># --------------------------------------------------------------------------</span>
<span class="token comment"># loss损失值优化</span>
<span class="token comment"># 输入参数：loss。learning_rate，学习速率。</span>
<span class="token comment"># 返回参数：train_op，训练op，这个参数要输入sess.run中让模型去训练。</span>
<span class="token keyword">def</span> <span class="token function">trainning</span><span class="token punctuation">(</span>loss<span class="token punctuation">,</span> learning_rate<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>name_scope<span class="token punctuation">(</span><span class="token string">'optimizer'</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
        optimizer <span class="token operator">=</span> tf<span class="token punctuation">.</span>train<span class="token punctuation">.</span>AdamOptimizer<span class="token punctuation">(</span>learning_rate<span class="token operator">=</span>learning_rate<span class="token punctuation">)</span>
        global_step <span class="token operator">=</span> tf<span class="token punctuation">.</span>Variable<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">,</span> name<span class="token operator">=</span><span class="token string">'global_step'</span><span class="token punctuation">,</span> trainable<span class="token operator">=</span><span class="token boolean">False</span><span class="token punctuation">)</span>
        train_op <span class="token operator">=</span> optimizer<span class="token punctuation">.</span>minimize<span class="token punctuation">(</span>loss<span class="token punctuation">,</span> global_step<span class="token operator">=</span>global_step<span class="token punctuation">)</span>
    <span class="token keyword">return</span> train_op


<span class="token comment"># -----------------------------------------------------------------------</span>
<span class="token comment"># 评价/准确率计算</span>
<span class="token comment"># 输入参数：logits，网络计算值。labels，标签，也就是真实值，在这里是0或者1。</span>
<span class="token comment"># 返回参数：accuracy，当前step的平均准确率，也就是在这些batch中多少张图片被正确分类了。</span>
<span class="token keyword">def</span> <span class="token function">evaluation</span><span class="token punctuation">(</span>logits<span class="token punctuation">,</span> labels<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>variable_scope<span class="token punctuation">(</span><span class="token string">'accuracy'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> scope<span class="token punctuation">:</span>
        correct <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>in_top_k<span class="token punctuation">(</span>logits<span class="token punctuation">,</span> labels<span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">)</span>
        correct <span class="token operator">=</span> tf<span class="token punctuation">.</span>cast<span class="token punctuation">(</span>correct<span class="token punctuation">,</span> tf<span class="token punctuation">.</span>float16<span class="token punctuation">)</span>
        accuracy <span class="token operator">=</span> tf<span class="token punctuation">.</span>reduce_mean<span class="token punctuation">(</span>correct<span class="token punctuation">)</span>
        tf<span class="token punctuation">.</span>summary<span class="token punctuation">.</span>scalar<span class="token punctuation">(</span>scope<span class="token punctuation">.</span>name <span class="token operator">+</span> <span class="token string">'/accuracy'</span><span class="token punctuation">,</span> accuracy<span class="token punctuation">)</span>
    <span class="token keyword">return</span> accuracy<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>

<p>train.py——利用D:/ML/flower/input_data/路径下的训练数据，对CNN模型进行训练</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token keyword">import</span> input_data
<span class="token keyword">import</span> model

<span class="token comment"># 变量声明</span>
N_CLASSES <span class="token operator">=</span> <span class="token number">4</span>  <span class="token comment"># 四种花类型</span>
IMG_W <span class="token operator">=</span> <span class="token number">64</span>  <span class="token comment"># resize图像，太大的话训练时间久</span>
IMG_H <span class="token operator">=</span> <span class="token number">64</span>
BATCH_SIZE <span class="token operator">=</span> <span class="token number">20</span>
CAPACITY <span class="token operator">=</span> <span class="token number">200</span>
MAX_STEP <span class="token operator">=</span> <span class="token number">2000</span>  <span class="token comment"># 一般大于10K</span>
learning_rate <span class="token operator">=</span> <span class="token number">0.0001</span>  <span class="token comment"># 一般小于0.0001</span>

<span class="token comment"># 获取批次batch</span>
train_dir <span class="token operator">=</span> <span class="token string">'F:/input_data'</span>  <span class="token comment"># 训练样本的读入路径</span>
logs_train_dir <span class="token operator">=</span> <span class="token string">'F:/save'</span>  <span class="token comment"># logs存储路径</span>

<span class="token comment"># train, train_label = input_data.get_files(train_dir)</span>
train<span class="token punctuation">,</span> train_label<span class="token punctuation">,</span> val<span class="token punctuation">,</span> val_label <span class="token operator">=</span> input_data<span class="token punctuation">.</span>get_files<span class="token punctuation">(</span>train_dir<span class="token punctuation">,</span> <span class="token number">0.3</span><span class="token punctuation">)</span>
<span class="token comment"># 训练数据及标签</span>
train_batch<span class="token punctuation">,</span> train_label_batch <span class="token operator">=</span> input_data<span class="token punctuation">.</span>get_batch<span class="token punctuation">(</span>train<span class="token punctuation">,</span> train_label<span class="token punctuation">,</span> IMG_W<span class="token punctuation">,</span> IMG_H<span class="token punctuation">,</span> BATCH_SIZE<span class="token punctuation">,</span> CAPACITY<span class="token punctuation">)</span>
<span class="token comment"># 测试数据及标签</span>
val_batch<span class="token punctuation">,</span> val_label_batch <span class="token operator">=</span> input_data<span class="token punctuation">.</span>get_batch<span class="token punctuation">(</span>val<span class="token punctuation">,</span> val_label<span class="token punctuation">,</span> IMG_W<span class="token punctuation">,</span> IMG_H<span class="token punctuation">,</span> BATCH_SIZE<span class="token punctuation">,</span> CAPACITY<span class="token punctuation">)</span>

<span class="token comment"># 训练操作定义</span>
train_logits <span class="token operator">=</span> model<span class="token punctuation">.</span>inference<span class="token punctuation">(</span>train_batch<span class="token punctuation">,</span> BATCH_SIZE<span class="token punctuation">,</span> N_CLASSES<span class="token punctuation">)</span>
train_loss <span class="token operator">=</span> model<span class="token punctuation">.</span>losses<span class="token punctuation">(</span>train_logits<span class="token punctuation">,</span> train_label_batch<span class="token punctuation">)</span>
train_op <span class="token operator">=</span> model<span class="token punctuation">.</span>trainning<span class="token punctuation">(</span>train_loss<span class="token punctuation">,</span> learning_rate<span class="token punctuation">)</span>
train_acc <span class="token operator">=</span> model<span class="token punctuation">.</span>evaluation<span class="token punctuation">(</span>train_logits<span class="token punctuation">,</span> train_label_batch<span class="token punctuation">)</span>

<span class="token comment"># 测试操作定义</span>
test_logits <span class="token operator">=</span> model<span class="token punctuation">.</span>inference<span class="token punctuation">(</span>val_batch<span class="token punctuation">,</span> BATCH_SIZE<span class="token punctuation">,</span> N_CLASSES<span class="token punctuation">)</span>
test_loss <span class="token operator">=</span> model<span class="token punctuation">.</span>losses<span class="token punctuation">(</span>test_logits<span class="token punctuation">,</span> val_label_batch<span class="token punctuation">)</span>
test_acc <span class="token operator">=</span> model<span class="token punctuation">.</span>evaluation<span class="token punctuation">(</span>test_logits<span class="token punctuation">,</span> val_label_batch<span class="token punctuation">)</span>

<span class="token comment"># 这个是log汇总记录</span>
summary_op <span class="token operator">=</span> tf<span class="token punctuation">.</span>summary<span class="token punctuation">.</span>merge_all<span class="token punctuation">(</span><span class="token punctuation">)</span>

<span class="token comment"># 产生一个会话</span>
sess <span class="token operator">=</span> tf<span class="token punctuation">.</span>Session<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token comment"># 产生一个writer来写log文件</span>
train_writer <span class="token operator">=</span> tf<span class="token punctuation">.</span>summary<span class="token punctuation">.</span>FileWriter<span class="token punctuation">(</span>logs_train_dir<span class="token punctuation">,</span> sess<span class="token punctuation">.</span>graph<span class="token punctuation">)</span>
<span class="token comment"># val_writer = tf.summary.FileWriter(logs_test_dir, sess.graph)</span>
<span class="token comment"># 产生一个saver来存储训练好的模型</span>
saver <span class="token operator">=</span> tf<span class="token punctuation">.</span>train<span class="token punctuation">.</span>Saver<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token comment"># 所有节点初始化</span>
sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>global_variables_initializer<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
<span class="token comment"># 队列监控</span>
coord <span class="token operator">=</span> tf<span class="token punctuation">.</span>train<span class="token punctuation">.</span>Coordinator<span class="token punctuation">(</span><span class="token punctuation">)</span>
threads <span class="token operator">=</span> tf<span class="token punctuation">.</span>train<span class="token punctuation">.</span>start_queue_runners<span class="token punctuation">(</span>sess<span class="token operator">=</span>sess<span class="token punctuation">,</span> coord<span class="token operator">=</span>coord<span class="token punctuation">)</span>

<span class="token comment"># 进行batch的训练</span>
<span class="token keyword">try</span><span class="token punctuation">:</span>
    <span class="token comment"># 执行MAX_STEP步的训练，一步一个batch</span>
    <span class="token keyword">for</span> step <span class="token keyword">in</span> np<span class="token punctuation">.</span>arange<span class="token punctuation">(</span>MAX_STEP<span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token keyword">if</span> coord<span class="token punctuation">.</span>should_stop<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
            <span class="token keyword">break</span>
        _<span class="token punctuation">,</span> tra_loss<span class="token punctuation">,</span> tra_acc <span class="token operator">=</span> sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span><span class="token punctuation">[</span>train_op<span class="token punctuation">,</span> train_loss<span class="token punctuation">,</span> train_acc<span class="token punctuation">]</span><span class="token punctuation">)</span>

        <span class="token comment"># 每隔50步打印一次当前的loss以及acc，同时记录log，写入writer</span>
        <span class="token keyword">if</span> step <span class="token operator">%</span> <span class="token number">10</span> <span class="token operator">==</span> <span class="token number">0</span><span class="token punctuation">:</span>
            <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'Step %d, train loss = %.2f, train accuracy = %.2f%%'</span> <span class="token operator">%</span> <span class="token punctuation">(</span>step<span class="token punctuation">,</span> tra_loss<span class="token punctuation">,</span> tra_acc <span class="token operator">*</span> <span class="token number">100.0</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
            summary_str <span class="token operator">=</span> sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>summary_op<span class="token punctuation">)</span>
            train_writer<span class="token punctuation">.</span>add_summary<span class="token punctuation">(</span>summary_str<span class="token punctuation">,</span> step<span class="token punctuation">)</span>
        <span class="token comment"># 每隔100步，保存一次训练好的模型</span>
        <span class="token keyword">if</span> <span class="token punctuation">(</span>step <span class="token operator">+</span> <span class="token number">1</span><span class="token punctuation">)</span> <span class="token operator">==</span> MAX_STEP<span class="token punctuation">:</span>
            checkpoint_path <span class="token operator">=</span> os<span class="token punctuation">.</span>path<span class="token punctuation">.</span>join<span class="token punctuation">(</span>logs_train_dir<span class="token punctuation">,</span> <span class="token string">'model.ckpt'</span><span class="token punctuation">)</span>
            saver<span class="token punctuation">.</span>save<span class="token punctuation">(</span>sess<span class="token punctuation">,</span> checkpoint_path<span class="token punctuation">,</span> global_step<span class="token operator">=</span>step<span class="token punctuation">)</span>

<span class="token keyword">except</span> tf<span class="token punctuation">.</span>errors<span class="token punctuation">.</span>OutOfRangeError<span class="token punctuation">:</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'Done training -- epoch limit reached'</span><span class="token punctuation">)</span>

<span class="token keyword">finally</span><span class="token punctuation">:</span>
    coord<span class="token punctuation">.</span>request_stop<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>



<p>test.py——利用D:/ML/flower/flower_photos/roses路径下的测试数据，查看识别效果</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token keyword">import</span> matplotlib<span class="token punctuation">.</span>pyplot <span class="token keyword">as</span> plt
<span class="token keyword">import</span> model
<span class="token keyword">from</span> input_data <span class="token keyword">import</span> get_files

<span class="token comment"># 获取一张图片</span>
<span class="token keyword">def</span> <span class="token function">get_one_image</span><span class="token punctuation">(</span>train<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token comment"># 输入参数：train,训练图片的路径</span>
    <span class="token comment"># 返回参数：image，从训练图片中随机抽取一张图片</span>
    n <span class="token operator">=</span> <span class="token builtin">len</span><span class="token punctuation">(</span>train<span class="token punctuation">)</span>
    ind <span class="token operator">=</span> np<span class="token punctuation">.</span>random<span class="token punctuation">.</span>randint<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">,</span> n<span class="token punctuation">)</span>
    img_dir <span class="token operator">=</span> train<span class="token punctuation">[</span>ind<span class="token punctuation">]</span>  <span class="token comment"># 随机选择测试的图片</span>

    img <span class="token operator">=</span> Image<span class="token punctuation">.</span><span class="token builtin">open</span><span class="token punctuation">(</span>img_dir<span class="token punctuation">)</span>
    plt<span class="token punctuation">.</span>imshow<span class="token punctuation">(</span>img<span class="token punctuation">)</span>
    plt<span class="token punctuation">.</span>show<span class="token punctuation">(</span><span class="token punctuation">)</span>
    image <span class="token operator">=</span> np<span class="token punctuation">.</span>array<span class="token punctuation">(</span>img<span class="token punctuation">)</span>
    <span class="token keyword">return</span> image


<span class="token comment"># 测试图片</span>
<span class="token keyword">def</span> <span class="token function">evaluate_one_image</span><span class="token punctuation">(</span>image_array<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">with</span> tf<span class="token punctuation">.</span>Graph<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">.</span>as_default<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
        BATCH_SIZE <span class="token operator">=</span> <span class="token number">1</span>
        N_CLASSES <span class="token operator">=</span> <span class="token number">4</span>

        image <span class="token operator">=</span> tf<span class="token punctuation">.</span>cast<span class="token punctuation">(</span>image_array<span class="token punctuation">,</span> tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>
        image <span class="token operator">=</span> tf<span class="token punctuation">.</span>image<span class="token punctuation">.</span>per_image_standardization<span class="token punctuation">(</span>image<span class="token punctuation">)</span>
        image <span class="token operator">=</span> tf<span class="token punctuation">.</span>reshape<span class="token punctuation">(</span>image<span class="token punctuation">,</span> <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">64</span><span class="token punctuation">,</span> <span class="token number">64</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">)</span>

        logit <span class="token operator">=</span> model<span class="token punctuation">.</span>inference<span class="token punctuation">(</span>image<span class="token punctuation">,</span> BATCH_SIZE<span class="token punctuation">,</span> N_CLASSES<span class="token punctuation">)</span>

        logit <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>softmax<span class="token punctuation">(</span>logit<span class="token punctuation">)</span>

        x <span class="token operator">=</span> tf<span class="token punctuation">.</span>placeholder<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">,</span> shape<span class="token operator">=</span><span class="token punctuation">[</span><span class="token number">64</span><span class="token punctuation">,</span> <span class="token number">64</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">)</span>

        <span class="token comment"># you need to change the directories to yours.</span>
        logs_train_dir <span class="token operator">=</span> <span class="token string">'F:/save/'</span>

        saver <span class="token operator">=</span> tf<span class="token punctuation">.</span>train<span class="token punctuation">.</span>Saver<span class="token punctuation">(</span><span class="token punctuation">)</span>

        <span class="token keyword">with</span> tf<span class="token punctuation">.</span>Session<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">as</span> sess<span class="token punctuation">:</span>

            <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"Reading checkpoints..."</span><span class="token punctuation">)</span>
            ckpt <span class="token operator">=</span> tf<span class="token punctuation">.</span>train<span class="token punctuation">.</span>get_checkpoint_state<span class="token punctuation">(</span>logs_train_dir<span class="token punctuation">)</span>
            <span class="token keyword">if</span> ckpt <span class="token keyword">and</span> ckpt<span class="token punctuation">.</span>model_checkpoint_path<span class="token punctuation">:</span>
                global_step <span class="token operator">=</span> ckpt<span class="token punctuation">.</span>model_checkpoint_path<span class="token punctuation">.</span>split<span class="token punctuation">(</span><span class="token string">'/'</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">.</span>split<span class="token punctuation">(</span><span class="token string">'-'</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">]</span>
                saver<span class="token punctuation">.</span>restore<span class="token punctuation">(</span>sess<span class="token punctuation">,</span> ckpt<span class="token punctuation">.</span>model_checkpoint_path<span class="token punctuation">)</span>
                <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'Loading success, global_step is %s'</span> <span class="token operator">%</span> global_step<span class="token punctuation">)</span>
            <span class="token keyword">else</span><span class="token punctuation">:</span>
                <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'No checkpoint file found'</span><span class="token punctuation">)</span>

            prediction <span class="token operator">=</span> sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>logit<span class="token punctuation">,</span> feed_dict<span class="token operator">=</span><span class="token punctuation">{</span>x<span class="token punctuation">:</span> image_array<span class="token punctuation">}</span><span class="token punctuation">)</span>
            max_index <span class="token operator">=</span> np<span class="token punctuation">.</span>argmax<span class="token punctuation">(</span>prediction<span class="token punctuation">)</span>
            <span class="token keyword">if</span> max_index <span class="token operator">==</span> <span class="token number">0</span><span class="token punctuation">:</span>
                result <span class="token operator">=</span> <span class="token punctuation">(</span><span class="token string">'这是玫瑰花的可能性为： %.6f'</span> <span class="token operator">%</span> prediction<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
            <span class="token keyword">elif</span> max_index <span class="token operator">==</span> <span class="token number">1</span><span class="token punctuation">:</span>
                result <span class="token operator">=</span> <span class="token punctuation">(</span><span class="token string">'这是郁金香的可能性为： %.6f'</span> <span class="token operator">%</span> prediction<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
            <span class="token keyword">elif</span> max_index <span class="token operator">==</span> <span class="token number">2</span><span class="token punctuation">:</span>
                result <span class="token operator">=</span> <span class="token punctuation">(</span><span class="token string">'这是蒲公英的可能性为： %.6f'</span> <span class="token operator">%</span> prediction<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
            <span class="token keyword">else</span><span class="token punctuation">:</span>
                result <span class="token operator">=</span> <span class="token punctuation">(</span><span class="token string">'这是这是向日葵的可能性为： %.6f'</span> <span class="token operator">%</span> prediction<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
            <span class="token keyword">return</span> result


<span class="token comment"># ------------------------------------------------------------------------</span>

<span class="token keyword">if</span> __name__ <span class="token operator">==</span> <span class="token string">'__main__'</span><span class="token punctuation">:</span>
    img <span class="token operator">=</span> Image<span class="token punctuation">.</span><span class="token builtin">open</span><span class="token punctuation">(</span><span class="token string">'F:/input_data/dandelion/1451samples2.jpg'</span><span class="token punctuation">)</span>
    plt<span class="token punctuation">.</span>imshow<span class="token punctuation">(</span>img<span class="token punctuation">)</span>
    plt<span class="token punctuation">.</span>show<span class="token punctuation">(</span><span class="token punctuation">)</span>
    imag <span class="token operator">=</span> img<span class="token punctuation">.</span>resize<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">64</span><span class="token punctuation">,</span> <span class="token number">64</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
    image <span class="token operator">=</span> np<span class="token punctuation">.</span>array<span class="token punctuation">(</span>imag<span class="token punctuation">)</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span>evaluate_one_image<span class="token punctuation">(</span>image<span class="token punctuation">)</span><span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>



<h3 id="项目执行结果："><a href="#项目执行结果：" class="headerlink" title="项目执行结果："></a>项目执行结果：</h3><p>1.执行train模块，结果如下：<br><img src="https://img-blog.csdnimg.cn/20190904022009654.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NyaW1zb25L,size_16,color_FFFFFF,t_70" alt="在这里插入图片描述"><br>同时，训练结束后，在电脑指定的训练模型存储路径可看到保存的训练好的模型数据。<br><img src="https://img-blog.csdnimg.cn/2019090402204926.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NyaW1zb25L,size_16,color_FFFFFF,t_70" alt="在这里插入图片描述"><br>2.执行test模块，结果如下：<br>显示一张测试用的图片<br><img src="https://img-blog.csdnimg.cn/20190904022121897.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NyaW1zb25L,size_16,color_FFFFFF,t_70" alt="在这里插入图片描述"><br>关闭显示的测试图片后，console查看测试结果如下：<br><img src="https://img-blog.csdnimg.cn/20190904022159603.png" alt="在这里插入图片描述"><br>至此我们对整个项目流程做一个总结：<br>图片预处理模块：对获得的花卉图片训练数据，进行预处理，构造训练用数据结构<br>训练模块：利用Tensorflow实现CNN（神经网络算法）模型，经过两层卷积-池化处理，并使用梯度下降算法作为优化器、Softmax算法作为分类器、平方损失函数（最小二乘法, Ordinary Least Squares）作为优化器，构建训练模型,利用训练数据对模型进行训练，最终得到训练后的模型数据，并以文件形式存储至本机。<br>分类准确度验证模块：利用Tensorflow的reduce_mean方法作为评估模型，对构建的花卉分类模型分类准确性进行验证。<br>模型测试模块：使用测试集数据，对构建并训练后的分类模型进行测试，验证实际数据的测试准确度。</p>
<p>具体代码以及附件可在我的个人GitHub上下载<br><a target="_blank" rel="noopener" href="https://github.com/beyou123">我的githubworkspace</a></p>
<p>原文地址：<a target="_blank" rel="noopener" href="https://blog.csdn.net/CrimsonK/article/details/100190807">https://blog.csdn.net/CrimsonK/article/details/100190807</a></p>
<p>二、<a target="_blank" rel="noopener" href="https://www.cnblogs.com/lijitao/protected/articles/12173520.html">https://www.cnblogs.com/lijitao/protected/articles/12173520.html</a></p>

                
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